Environment Department, University of York, Heslington, York, UK.
Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Akoka, Lagos, Nigeria.
Environ Sci Pollut Res Int. 2020 Jan;27(1):66-74. doi: 10.1007/s11356-019-05589-x. Epub 2019 Jun 14.
For several years, Landsat imageries have been used for land cover mapping analysis. However, cloud cover constitutes a major obstacle to land cover classification in coastal tropical regions including Lagos State. In this work, a land cover appearance for Lagos State is examined using Sentinel-1 synthetic aperture radar (SAR) and Land Satellite 8 (Landsat 8) imageries. To this aim, a Sentinel-1 SAR dual-pol (VV+VH) Interferometric Wide swath mode (IW) data orbit for 2017 and a Landsat 8 Operational Land Imager (OLI) for 2017 over Lagos State were acquired and analysed. The Sentinel-1 imagery was calibrated and terrain corrected using a SRTM 3Sec DEM. Maximum likelihood classification algorithm was performed. A supervised pixel-based imagery classification to classify the dataset using training points selected from RGB combination of VV and VH polarizations was applied. Accuracy assessment was performed using test data collected from high-resolution imagery of Google Earth to determine the overall classification accuracy and Kappa coefficient. The Landsat 8 was orthorectified and maximum likelihood classification algorithm also performed. The results for Sentinel-1 include an RGB composite of the imagery, classified imagery, with overall accuracy calculated as 0.757, while the kappa value was evaluated to be about 0.719. Also, the Landsat 8 includes a RBG composite of the imagery, classified imagery, but an overall accuracy of 0.908 and a kappa value of 0.876. It is concluded that Sentinel 1 SAR result has been effectively exploited for producing acceptable accurate land cover map of Lagos State with relevant advantages for areas with cloud cover. In addition, the Landsat 8 result reported a high accuracy assessment values with finer visual land cover map appearance.
几年来，Landsat 成像仪一直被用于土地覆盖制图分析。然而，云覆盖是包括拉各斯州在内的热带沿海地区土地覆盖分类的主要障碍。在这项工作中，使用 Sentinel-1 合成孔径雷达 (SAR) 和陆地卫星 8 (Landsat 8) 图像检查了拉各斯州的土地覆盖外观。为此，获取并分析了 2017 年的 Sentinel-1 SAR 双极化（VV+VH）干涉宽幅模式（IW）轨道和 2017 年的 Landsat 8 运行陆地成像仪（OLI）数据。Sentinel-1 图像使用 SRTM 3Sec DEM 进行校准和地形校正。使用最大似然分类算法进行分类。使用从 VV 和 VH 极化的 RGB 组合中选择的训练点对数据集进行监督像素图像分类，应用基于像素的图像分类。使用从谷歌地球的高分辨率图像收集的测试数据进行准确性评估，以确定总体分类准确性和 Kappa 系数。对 Landsat 8 进行了正射校正，并应用了最大似然分类算法。Sentinel-1 的结果包括图像的 RGB 合成，分类图像，总体精度计算为 0.757，而 Kappa 值评估约为 0.719。此外，Landsat 8 包括图像的 RGB 合成，分类图像，但总体精度为 0.908，Kappa 值为 0.876。结论是，有效地利用 Sentinel 1 SAR 结果为拉各斯州生成了可接受的准确土地覆盖图，为有云覆盖的地区提供了相关优势。此外，Landsat 8 报告的精度评估值较高，土地覆盖图的视觉效果更精细。
